Video Event Recognition by Dempster-Shafer Theory

Xin Hong, Yan Huang, WenJun Ma, Paul Miller, Weiru Liu, Huiyu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
132 Downloads (Pure)


This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.
Original languageEnglish
Title of host publication21 European Conference on Artificial Intelligence (ECAI 2014)
Number of pages2
Publication statusPublished - Aug 2014
EventEuropean Conference on Artificial Intelligence (ECAI) - , Czech Republic
Duration: 18 Aug 201422 Aug 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications


ConferenceEuropean Conference on Artificial Intelligence (ECAI)
Country/TerritoryCzech Republic


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